目的:在常规结构磁共振成像(MRI)中没有明确局灶性病变的耐药局灶性癫痫(DRFE)的儿科患者是一个特别具有挑战性的队列,以治疗和构成癫痫手术计划的一部分。最近开发的基于深度学习的MRI病变检测算法,多中心病变检测(MELD)算法,已被证明有助于检测局灶性皮质发育不良(FCD)。我们将该算法应用于接受立体脑电图(SEEG)的难治性局灶性癫痫的MRI阴性儿童队列,以确定其识别未见癫痫性病变的准确性。癫痫发作区和临床结果。
方法:我们对在三级儿科癫痫手术中心接受SEEG的一系列MRI阴性患者进行了回顾性分析。我们评估了确定的MELD簇或病变区域与临床癫痫发作假设相对应的程度。癫痫网络,和正电子发射断层扫描(PET)局灶性低代谢区。在那些接受了切除手术的人中,我们分析了MELD异常区域是否与手术目标相对应,以及在多大程度上与无癫痫发作相关.
结果:我们在28名MRI阴性儿童中确定了37项SEEG研究,我们可以在其中运行MELD算法。其中,14名(50%)儿童在MELD上发现了集群。9名(32%)儿童有与癫痫发作假说一致的集群,6例(21%)与PET成像一致,5例(18%)儿童至少有一个与SEEG电极放置一致的簇。总的来说,4个独立儿童中的4个MELD集群基于SEEG刺激数据正确预测了癫痫发作发作区或刺激区。16名儿童(57%)继续进行切除或病灶手术。其中,只有1例(4%)患者的MELD集群与切除腔共定位,该儿童的结果为Engel1A.
结论:在我们的MRI阴性耐药局灶性癫痫患者的儿科队列中,MELD算法在一半的病例中识别出异常簇或病变,并确定了一个放射学隐匿性局灶性皮质发育不良。基于机器学习的病变检测是一个有前途的研究领域,有可能在这个具有挑战性的放射学隐匿性FCD病例队列中改善癫痫发作的结果。然而,应谨慎对待其应用,特别是关于其在检测FCD病变中的特异性,在增加诊断实用程序之前,还有很多工作要做。
OBJECTIVE: Paediatric patients with drug-resistant focal epilepsy (DRFE) who have no clear focal lesion identified on conventional structural magnetic resonance imaging (MRI) are a particularly challenging cohort to treat and form an increasing part of epilepsy surgery programs. A recently developed deep-learning-based MRI lesion detection algorithm, the Multicentre Lesion Detection (
MELD) algorithm, has been shown to aid detection of focal cortical dysplasia (FCD). We applied this algorithm retrospectively to a cohort of MRI-negative children with refractory focal epilepsy who underwent stereoelectroencephalography (SEEG) to determine its accuracy in identifying unseen epileptic lesions, seizure onset zones and clinical outcomes.
METHODS: We retrospectively applied the
MELD algorithm to a consecutive series of MRI-negative patients who underwent SEEG at our tertiary Paediatric Epilepsy Surgery centre. We assessed the extent to which the identified
MELD cluster or lesion area corresponded with the clinical seizure hypothesis, the epileptic network, and the positron emission tomography (PET) focal hypometabolic area. In those who underwent resective surgery, we analysed whether the region of MELD abnormality corresponded with the surgical target and to what extent this was associated with seizure freedom.
RESULTS: We identified 37 SEEG studies in 28 MRI-negative children in whom we could run the MELD algorithm. Of these, 14 (50 %) children had clusters identified on
MELD. Nine (32 %) children had clusters concordant with seizure hypothesis, 6 (21 %) had clusters concordant with PET imaging, and 5 (18 %) children had at least one cluster concordant with SEEG electrode placement. Overall, 4 MELD clusters in 4 separate children correctly predicted either seizure onset zone or irritative zone based on SEEG stimulation data. Sixteen children (57 %) went on to have resective or lesional surgery. Of these, only one patient (4 %) had a
MELD cluster which co-localised with the resection cavity and this child had an Engel 1 A outcome.
CONCLUSIONS: In our paediatric cohort of MRI-negative patients with drug-resistant focal epilepsy, the MELD algorithm identified abnormal clusters or lesions in half of cases, and identified one radiologically occult focal cortical dysplasia. Machine-learning-based lesion detection is a promising area of research with the potential to improve seizure outcomes in this challenging cohort of radiologically occult FCD cases. However, its application should be approached with caution, especially with regards to its specificity in detecting FCD lesions, and there is still work to be done before it adds to diagnostic utility.